Program Development of Digital Leadership for School Administrators Under the office of Primary Educational Service Area
Bibliographic record
Abstract
The purposes of the research aim to 1) To study the components and indicators of digital leadership of school administrators 2) To study the current condition desirable conditions and necessities for digital leadership of school administrators 3) Develop a digital leadership development program for school administrators 4) To study the effect of using digital leadership development program of school administrators. Results of the research are as follows: 1) Digital leadership of school administrators consist of 7 components, 22 indicators 2) Desirable Conditions for Digital Leadership of School Administrators. The overall average was at a low level. Desirable Conditions for Digital Leadership of School Administrators Overall, it's at the highest level. and the necessity of developing digital leadership among school administrators, the highest value was digital vision leadership, 3) Program development of digital leadership for school administrators is suitable possible and is useful overall, it was at the highest level. 4) The results of using the program; 4.1) Program development of digital leadership for school administrators. The efficiency was 93.01/95.55, which was higher than the 80/80 threshold set. 4.2) The effectiveness index in the program was 0.9306 or equivalent to 93.06 percent. 4.3) Program development of digital leadership for school administrators has higher academic achievement after school than before with statistical significance at the .05 level. 4.4) Executives developed with the program was no difference in educational achievement scores after 2 weeks of study. 4.5) Satisfaction of school administrators, Overall, the satisfaction level was at the highest level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".